Image Analysis Tools in Proteomics

Andrew W Dowsey, Imperial College London, London, UK
Jane A English, University College Dublin, Belfield, Ireland
Frederique Lisacek, Swiss Institute of Bioinformatics, Geneva, Switzerland
Jeffrey S Morris, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
Guang‐Zhong Yang, Imperial College London, London, UK
Michael J Dunn, University College Dublin, Belfield, Ireland

Published online: January 2011

DOI: 10.1002/9780470015902.a0006216.pub2

Image analysis tools have matured into a number of established commercial packages and freely available programs that underpin research in expression proteomics. With respect to the available tools, we describe the challenges and methods for image analysis in two-dimensional gel electrophoresis and the emerging high-throughput ‘shotgun’ proteomics platform of liquid chromatography coupled to mass spectrometry. Two workflows can be identified: the established pipeline of spot detection followed by spot matching, and the new alternative pipeline of image alignment followed by consensus spot detection. In practice, image analysis is often viewed as a major bottleneck in proteomics, and we will further touch on emerging research that aims to statistically model and integrate data earlier in the pipeline to minimise manual interaction and therefore the subjective bias and restrictions that it brings.

Key Concepts:

  • Precise quantification and differential analysis of protein expression is reliant on semiautomated image analysis tools.
  • Liquid chromatography/mass spectrometry (LC/MS) datasets can be processed in a similar manner to 2-D gel electrophoresis (2-DE) by conversion to images with the mass to charge ratio on one axis and retention time on the other.
  • The conventional image analysis workflow involves detection of protein/peptide spots on each dataset followed by matching of corresponding spots between each dataset and a designated reference.
  • An alternative workflow has emerged, which warps the images into alignment before determining a consensus spot detection on the set of images as a whole.
  • It is important for all researchers to understand the differing unique challenges for analysis tools posed by 2-DE and LC/MS, such as varying operating conditions, artefacts and contaminants.
  • LC/MS analyses also include peptide deisotoping and charge state estimation steps, plus computation of normalised retention times for improved peptide identification.
  • Sound statistical treatment of the results is key, including the use of the false discovery rate correction for overcoming the multiple hypothesis problem.
  • With existing approaches, errors at each stage propagate and amplify as the pipeline progresses; therefore, emerging techniques are aiming to provide an integrated consensus analysis that borrows strength across the image set.
  • Consensus pinnacle detection and image-based alignment and deconvolution are novel methods that can provide automated, reliable and robust quantification.
  • Functional data analysis presents a new statistical paradigm for image-based differential detection able to compensate for multiple experimental factors and discover protein regulation in comigrated regions missed by spot-centric approaches.

Keywords: proteomics; two-dimensional electrophoresis; liquid chromatography; mass spectrometry; image analysis; proteome informatics; spot detection; spot matching; chromatogram alignment; image registration

Figure 1. Comparison of the spot detection and matching workflow of Melanie with the image alignment and consensus spot detection workflow of Progenesis SameSpots. Reproduced with permission from Dowsey et al. (2010). Copyright Wiley-VCH Verlag GmbH & Co. KGaA.
Figure 2. Illustration of the profile (left) and 3D view (right) features in Melanie 4.
Figure 3. Qualitative and quantitative comparisons in Melanie 4. Images can be shown in stacked and/or tiled modes. The dual-channel display is shown at the lower left corner of the program window. Selected objects (highlighted in green on the screen) can be viewed on gels, histograms and reports.
Figure 4. MSight workflow. (a) The 3D view highlights aid quality control of the input data and results. The alignment procedure is based on the use of landmarks to compensate for differences in elution time or migration distance. Small letters ‘a’ to ‘e’ are potential landmarks. (b) Thus, the peak detection algorithm looks for areas of high-intensity peaks to delineate their shapes. The deisotoping step then looks for the monoisotopic peaks of the same molecule, links them together (dashed lines connect isotopes) and determines ion charge states. Reproduced with permission from Dowsey et al. (2010). Copyright Wiley-VCH Verlag GmbH & Co. KGaA.
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Related Sub-Topics:

Basic Cell Biology, Protein, RNA and DNA | Techniques in Cell Biology | Techniques and Tools in Molecular Biology | Biochemical and Biophysical Techniques | Proteins: Structure, Function, Metabolism
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Article Title: Image Analysis Tools in Proteomics
 
How to Cite close
Dowsey, Andrew W, English, Jane A, Lisacek, Frederique, Morris, Jeffrey S, Yang, Guang‐Zhong, and Dunn, Michael J(Jan 2011) Image Analysis Tools in Proteomics. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0006216.pub2]